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/*! \file
  \brief Functor performing linear scaling operations used by epilogues. Values
  are clamped before converting to the output element type.
*/

#pragma once

#include "cutlass/cutlass.h"
#include "cutlass/numeric_types.h"
#include "cutlass/array.h"
#include "cutlass/functional.h"
#include "cutlass/numeric_conversion.h"

/////////////////////////////////////////////////////////////////////////////////////////////////

namespace cutlass {
namespace epilogue {
namespace thread {

/////////////////////////////////////////////////////////////////////////////////////////////////

/// Applies a linear combination operator to an array of elements then clamps
/// the output before converting to the output element type.
///
/// D = alpha * accumulator + beta * source + uniform
///
template <typename ElementOutput_,  ///< Data type used to load and store
                                    ///< tensors
          int Count,  ///< Number of elements computed per operation
          typename ElementAccumulator_ =
                  ElementOutput_,  ///< Accumulator data type
          typename ElementCompute_ =
                  ElementOutput_,  ///< Data type used to compute linear
                                   ///< combination
          FloatRoundStyle Round = FloatRoundStyle::round_to_nearest>
class LinearCombinationClamp {
public:
    using ElementOutput = ElementOutput_;
    using ElementAccumulator = ElementAccumulator_;
    using ElementCompute = ElementCompute_;

    static int const kCount = Count;

    using FragmentOutput = Array<ElementOutput, kCount>;
    using FragmentAccumulator = Array<ElementAccumulator, kCount>;
    using ComputeFragment = Array<ElementCompute, kCount>;

    static FloatRoundStyle const kRound = Round;

    /// Host-constructable parameters structure
    struct Params {
        ElementCompute alpha;             ///< scales accumulators
        ElementCompute beta;              ///< scales source tensor
        ElementCompute const* alpha_ptr;  ///< pointer to accumulator scalar -
                                          ///< if not null, loads it from memory
        ElementCompute const* beta_ptr;   ///< pointer to source scalar - if not
                                          ///< null, loads it from memory

        //
        // Methods
        //

        CUTLASS_HOST_DEVICE
        Params()
                : alpha(ElementCompute(1)),
                  beta(ElementCompute(0)),
                  alpha_ptr(nullptr),
                  beta_ptr(nullptr) {}

        CUTLASS_HOST_DEVICE
        Params(ElementCompute alpha, ElementCompute beta)
                : alpha(alpha),
                  beta(beta),
                  alpha_ptr(nullptr),
                  beta_ptr(nullptr) {}

        CUTLASS_HOST_DEVICE
        Params(ElementCompute const* alpha_ptr, ElementCompute const* beta_ptr)
                : alpha(0), beta(0), alpha_ptr(alpha_ptr), beta_ptr(beta_ptr) {}
    };

private:
    //
    // Data members
    //

    ElementCompute alpha_;
    ElementCompute beta_;

public:
    /// Constructs the function object, possibly loading from pointers in host
    /// memory
    CUTLASS_HOST_DEVICE
    LinearCombinationClamp(Params const& params) {
        alpha_ = (params.alpha_ptr ? *params.alpha_ptr : params.alpha);
        beta_ = (params.beta_ptr ? *params.beta_ptr : params.beta);
    }

    /// Returns true if source is needed
    CUTLASS_HOST_DEVICE
    bool is_source_needed() const { return beta_ != ElementCompute(0); }

    /// Functionally required for serial reduction in the epilogue
    CUTLASS_HOST_DEVICE
    void set_k_partition(int k_partition, int k_partition_count) {
        if (k_partition) {
            beta_ = ElementCompute(1);
        }
    }

    /// Computes linear scaling: D = alpha * accumulator + beta * source
    CUTLASS_HOST_DEVICE
    FragmentOutput operator()(
            FragmentAccumulator const& accumulator,
            FragmentOutput const& source,
            ElementCompute uniform = ElementCompute(0)) const {
        // Convert source to interal compute numeric type
        NumericArrayConverter<ElementCompute, ElementOutput, kCount, Round>
                source_converter;
        NumericArrayConverter<ElementCompute, ElementAccumulator, kCount, Round>
                accumulator_converter;

        ComputeFragment converted_source = source_converter(source);
        ComputeFragment converted_accumulator =
                accumulator_converter(accumulator);

        // Perform binary operations

        ComputeFragment intermediate;

        multiplies<ComputeFragment> mul_add_source;
        multiply_add<ComputeFragment> mul_add_accumulator;

        minimum<ComputeFragment> min_accumulator;
        maximum<ComputeFragment> max_accumulator;

        intermediate = mul_add_source(
                beta_, converted_source);  // X =  beta * C + uniform
        intermediate =
                mul_add_accumulator(alpha_, converted_accumulator,
                                    intermediate);  // D = alpha * Accum + X

        /// Clamping constant value
        ElementCompute const kClamp = ElementCompute(
                (1U << (sizeof_bits<ElementOutput>::value - 1)) - 1);

        intermediate =
                max_accumulator(intermediate, -kClamp - ElementCompute(1));
        intermediate = min_accumulator(intermediate, kClamp);

        // Convert to destination numeric type
        NumericArrayConverter<ElementOutput, ElementCompute, kCount, Round>
                destination_converter;

        return destination_converter(intermediate);
    }

    /// Computes linear scaling: D = alpha * accumulator
    CUTLASS_HOST_DEVICE
    FragmentOutput operator()(FragmentAccumulator const& accumulator) const {
        // Convert source to interal compute numeric type
        NumericArrayConverter<ElementCompute, ElementAccumulator, kCount, Round>
                accumulator_converter;

        ComputeFragment converted_accumulator =
                accumulator_converter(accumulator);

        // Perform binary operations

        ComputeFragment intermediate;

        multiplies<ComputeFragment> mul_accumulator;

        minimum<ComputeFragment> min_accumulator;
        maximum<ComputeFragment> max_accumulator;

        intermediate = mul_accumulator(
                alpha_, converted_accumulator);  // D = alpha * Accum

        /// Clamping constant value
        ElementCompute const kClamp = ElementCompute(
                (1U << (sizeof_bits<ElementOutput>::value - 1)) - 1);

        intermediate =
                max_accumulator(intermediate, -kClamp - ElementCompute(1));
        intermediate = min_accumulator(intermediate, kClamp);

        // Convert to destination numeric type
        NumericArrayConverter<ElementOutput, ElementCompute, kCount, Round>
                destination_converter;

        return destination_converter(intermediate);
    }
};

/////////////////////////////////////////////////////////////////////////////////////////////////

// Conditional guards to enable partial specialization for packed integers
#if defined(__CUDA_ARCH__) && (__CUDA_ARCH__ >= 720) && \
        ((__CUDACC_VER_MAJOR__ > 10) ||                 \
         ((__CUDACC_VER_MAJOR__ >= 10) && (__CUDACC_VER_MINOR__ >= 2)))

/// Applies a linear combination operator to an array of elements then clamps
/// the output before converting to the output element type.
///
/// D = alpha * accumulator + beta * source + uniform
///
template <typename ElementOutput_,  ///< Data type used to load and store
                                    ///< tensors
          int Count,  ///< Number of elements computed per operation
          FloatRoundStyle Round>
class LinearCombinationClamp<ElementOutput_, Count, int, float, Round> {
public:
    using ElementOutput = ElementOutput_;
    using ElementAccumulator = int;
    using ElementCompute = float;

    static_assert(
            platform::is_same<ElementOutput, int32_t>::value ||
                    platform::is_same<ElementOutput, uint32_t>::value ||
                    platform::is_same<ElementOutput, int16_t>::value ||
                    platform::is_same<ElementOutput, uint16_t>::value ||
                    platform::is_same<ElementOutput, int8_t>::value ||
                    platform::is_same<ElementOutput, uint8_t>::value ||
                    platform::is_same<ElementOutput, cutlass::int4b_t>::value ||
                    platform::is_same<ElementOutput,
                                      cutlass::uint4b_t>::value ||
                    platform::is_same<ElementOutput, cutlass::uint1b_t>::value,
            "This elementwise op expects the output to be int.");

    static int const kCount = Count;

    using FragmentOutput = Array<ElementOutput, kCount>;
    using FragmentAccumulator = Array<ElementAccumulator, kCount>;
    using ComputeFragment = Array<ElementCompute, kCount>;

    static FloatRoundStyle const kRound = Round;

    /// Host-constructable parameters structure
    struct Params {
        ElementCompute alpha;             ///< scales accumulators
        ElementCompute beta;              ///< scales source tensor
        ElementCompute const* alpha_ptr;  ///< pointer to accumulator scalar -
                                          ///< if not null, loads it from memory
        ElementCompute const* beta_ptr;   ///< pointer to source scalar - if not
                                          ///< null, loads it from memory

        //
        // Methods
        //

        CUTLASS_HOST_DEVICE
        Params()
                : alpha(ElementCompute(1)),
                  beta(ElementCompute(0)),
                  alpha_ptr(nullptr),
                  beta_ptr(nullptr) {}

        CUTLASS_HOST_DEVICE
        Params(ElementCompute alpha, ElementCompute beta)
                : alpha(alpha),
                  beta(beta),
                  alpha_ptr(nullptr),
                  beta_ptr(nullptr) {}

        CUTLASS_HOST_DEVICE
        Params(ElementCompute const* alpha_ptr, ElementCompute const* beta_ptr)
                : alpha(0), beta(0), alpha_ptr(alpha_ptr), beta_ptr(beta_ptr) {}
    };

private:
    //
    // Data members
    //

    ElementCompute alpha_;
    ElementCompute beta_;

public:
    /// Constructs the function object, possibly loading from pointers in host
    /// memory
    CUTLASS_HOST_DEVICE
    LinearCombinationClamp(Params const& params) {
        alpha_ = (params.alpha_ptr ? *params.alpha_ptr : params.alpha);
        beta_ = (params.beta_ptr ? *params.beta_ptr : params.beta);
    }

    /// Returns true if source is needed
    CUTLASS_HOST_DEVICE
    bool is_source_needed() const { return beta_ != ElementCompute(0); }

    /// Functionally required for serial reduction in the epilogue
    CUTLASS_HOST_DEVICE
    void set_k_partition(int k_partition, int k_partition_count) {
        if (k_partition) {
            beta_ = ElementCompute(1);
        }
    }

    /// Computes linear scaling: D = alpha * accumulator + beta * source
    CUTLASS_HOST_DEVICE
    FragmentOutput operator()(
            FragmentAccumulator const& accumulator,
            FragmentOutput const& source,
            ElementCompute uniform = ElementCompute(0)) const {
        // Convert source to interal compute numeric type
        NumericArrayConverter<ElementCompute, ElementOutput, kCount, Round>
                source_converter;
        NumericArrayConverter<ElementCompute, ElementAccumulator, kCount, Round>
                accumulator_converter;

        ComputeFragment converted_source = source_converter(source);
        ComputeFragment converted_accumulator =
                accumulator_converter(accumulator);

        // Compute linear scaling in floating point
        ComputeFragment intermediate;

        multiplies<ComputeFragment> mul_add_source;
        multiply_add<ComputeFragment> mul_add_accumulator;

        // Float min-max
        intermediate = mul_add_source(
                beta_, converted_source);  // X =  beta * C + uniform
        intermediate =
                mul_add_accumulator(alpha_, converted_accumulator,
                                    intermediate);  // D = alpha * Accum + X

        // Convert floats back to INT
        FragmentAccumulator scaled_accumulator;

        CUTLASS_PRAGMA_UNROLL
        for (int i = 0; i < kCount; ++i) {
            scaled_accumulator[i] = __float2int_rn(intermediate[i]);
        }

        // Convert to destination numeric type
        NumericArrayConverter<ElementOutput, int, kCount, Round>
                destination_converter;

        return destination_converter(scaled_accumulator);
    }

    /// Computes linear scaling: D = alpha * accumulator
    CUTLASS_HOST_DEVICE
    FragmentOutput operator()(FragmentAccumulator const& accumulator) const {
        // Convert source to interal compute numeric type
        NumericArrayConverter<ElementCompute, ElementAccumulator, kCount, Round>
                accumulator_converter;

        ComputeFragment converted_accumulator =
                accumulator_converter(accumulator);

        // Compute linear scaling in floating point
        ComputeFragment intermediate;

        multiplies<ComputeFragment> mul_add_accumulator;

        // Float min-max
        intermediate = mul_add_accumulator(
                alpha_, converted_accumulator);  // D = alpha * Accum

        // Convert floats back to INT
        FragmentAccumulator scaled_accumulator;

        CUTLASS_PRAGMA_UNROLL
        for (int i = 0; i < kCount; ++i) {
            scaled_accumulator[i] = __float2int_rn(intermediate[i]);
        }

        // Convert to destination numeric type
        NumericArrayConverter<ElementOutput, int, kCount, Round>
                destination_converter;

        return destination_converter(scaled_accumulator);
    }
};

#endif  // Conditional guards to enable partial specialization for packed
        // integers

////////////////////////////////////////////////////////////////////////////////

/// Applies a linear combination operator to an array of elements then clamps
/// the output before converting to the output element type.
///
/// D = alpha * accumulator + beta * source + uniform
///
/// Note: The below method only when problem_size_K <= 256 for signed int8 gemm
/// or problem_size_K <= 128 for unsigned int8 gemm. The default approach is
/// above.
/// TODO: Add logic to fallback to the default approach
template <
        /// Data type used to load and store< tensors
        typename ElementOutput_,
        /// Number of elements computed per operation
        int Count,
        /// Rounding mode
        FloatRoundStyle Round = FloatRoundStyle::round_to_nearest>
class FastLinearCombinationClamp {
public:
    using ElementOutput = ElementOutput_;
    using ElementAccumulator = int;
    using ElementCompute = float;

    static_assert(
            platform::is_same<ElementOutput, int32_t>::value ||
                    platform::is_same<ElementOutput, uint32_t>::value ||
                    platform::is_same<ElementOutput, int16_t>::value ||
                    platform::is_same<ElementOutput, uint16_t>::value ||
                    platform::is_same<ElementOutput, int8_t>::value ||
                    platform::is_same<ElementOutput, uint8_t>::value ||
                    platform::is_same<ElementOutput, cutlass::int4b_t>::value ||
                    platform::is_same<ElementOutput,
                                      cutlass::uint4b_t>::value ||
                    platform::is_same<ElementOutput, cutlass::uint1b_t>::value,
            "This elementwise op expects the output to be int.");

    static int const kCount = Count;

    using FragmentOutput = Array<ElementOutput, kCount>;
    using FragmentAccumulator = Array<ElementAccumulator, kCount>;
    using ComputeFragment = Array<ElementCompute, kCount>;

    static FloatRoundStyle const kRound = Round;

    /// Host-constructable parameters structure
    struct Params {
        /// scales accumulators
        ElementCompute alpha;
        /// scales source tensor
        ElementCompute beta;
        /// pointer to accumulator scalar - if not null, loads it from memory
        ElementCompute const* alpha_ptr;
        /// pointer to source scalar - if not null, loads it from memory
        ElementCompute const* beta_ptr;

        //
        // Methods
        //

        CUTLASS_HOST_DEVICE
        Params()
                : alpha(ElementCompute(1)),
                  beta(ElementCompute(0)),
                  alpha_ptr(nullptr),
                  beta_ptr(nullptr) {}

        CUTLASS_HOST_DEVICE
        Params(ElementCompute alpha, ElementCompute beta)
                : alpha(alpha),
                  beta(beta),
                  alpha_ptr(nullptr),
                  beta_ptr(nullptr) {}

        CUTLASS_HOST_DEVICE
        Params(ElementCompute const* alpha_ptr, ElementCompute const* beta_ptr)
                : alpha(0), beta(0), alpha_ptr(alpha_ptr), beta_ptr(beta_ptr) {}
    };

private:
    //
    // Data members
    //

    ElementCompute alpha_;
    ElementCompute beta_;

public:
    /// Constructs the function object, possibly loading from pointers in host
    /// memory
    CUTLASS_HOST_DEVICE
    FastLinearCombinationClamp(Params const& params) {
        alpha_ = (params.alpha_ptr ? *params.alpha_ptr : params.alpha);
        beta_ = (params.beta_ptr ? *params.beta_ptr : params.beta);
    }

    /// Returns true if source is needed
    CUTLASS_HOST_DEVICE
    bool is_source_needed() const { return beta_ != ElementCompute(0); }

    /// Functionally required for serial reduction in the epilogue
    CUTLASS_HOST_DEVICE
    void set_k_partition(int k_partition, int k_partition_count) {
        if (k_partition) {
            beta_ = ElementCompute(1);
        }
    }

    /// Computes linear scaling: D = alpha * accumulator + beta * source
    CUTLASS_HOST_DEVICE
    FragmentOutput operator()(
            FragmentAccumulator const& accumulator,
            FragmentOutput const& source,
            ElementCompute uniform = ElementCompute(0)) const {
        // Convert source to interal compute numeric type
        FastNumericArrayConverter<ElementCompute, ElementOutput, kCount, Round>
                source_converter;
        FastNumericArrayConverter<ElementCompute, ElementAccumulator, kCount,
                                  Round>
                accumulator_converter;

        ComputeFragment converted_source = source_converter(source);
        ComputeFragment converted_accumulator =
                accumulator_converter(accumulator);

        // Compute linear scaling in floating point
        ComputeFragment intermediate;

        multiplies<ComputeFragment> mul_add_source;
        multiply_add<ComputeFragment> mul_add_accumulator;

        minimum<ComputeFragment> min_accumulator;
        maximum<ComputeFragment> max_accumulator;

        // Float min-max
        intermediate = mul_add_source(
                beta_, converted_source);  // X =  beta * C + uniform
        intermediate =
                mul_add_accumulator(alpha_, converted_accumulator,
                                    intermediate);  // D = alpha * Accum + X

        /// Clamping constant value
        ElementCompute const kClampMax =
                ElementCompute(platform::numeric_limits<ElementOutput>::max());

        ElementCompute const kClampMin = ElementCompute(
                platform::numeric_limits<ElementOutput>::lowest());

        intermediate = max_accumulator(intermediate, kClampMin);
        intermediate = min_accumulator(intermediate, kClampMax);

        // Convert to destination numeric type
        FastNumericArrayConverter<ElementOutput, ElementCompute, kCount, Round>
                destination_converter;

        return destination_converter(intermediate);
    }

    /// Computes linear scaling: D = alpha * accumulator + beta * source
    CUTLASS_HOST_DEVICE
    FragmentOutput operator()(FragmentAccumulator const& accumulator) const {
        // Convert source to interal compute numeric type
        FastNumericArrayConverter<ElementCompute, ElementAccumulator, kCount,
                                  Round>
                accumulator_converter;

        ComputeFragment converted_accumulator =
                accumulator_converter(accumulator);

        // Compute linear scaling in floating point
        ComputeFragment intermediate;

        multiplies<ComputeFragment> mul_accumulator;

        minimum<ComputeFragment> min_accumulator;
        maximum<ComputeFragment> max_accumulator;

        // Float min-max
        intermediate = mul_accumulator(alpha_, converted_accumulator);

        /// Clamping constant value
        ElementCompute const kClampMax =
                ElementCompute(platform::numeric_limits<ElementOutput>::max());

        ElementCompute const kClampMin = ElementCompute(
                platform::numeric_limits<ElementOutput>::lowest());

        intermediate = max_accumulator(intermediate, kClampMin);
        intermediate = min_accumulator(intermediate, kClampMax);

        // Convert to destination numeric type
        FastNumericArrayConverter<ElementOutput, ElementCompute, kCount, Round>
                destination_converter;

        return destination_converter(intermediate);
    }
};

////////////////////////////////////////////////////////////////////////////////

}  // namespace thread
}  // namespace epilogue
}  // namespace cutlass
